Download presentation

Presentation is loading. Please wait.

Published byKennedi Bartell Modified over 2 years ago

1
1 Combinatorial Agency with Audits Raphael Eidenbenz ETH Zurich, Switzerland Stefan Schmid TU Munich, Germany TexPoint fonts used in EMF. Read the TexPoint manual before you delete this box.: AA A A A A A

2
Raphael Eidenbenz, GameNets ‘092 Introduction Grid Computing... –Distributed project orchestrated by one server –Server distributes tasks –Agents compute subtask –Results are sent back to server –Server aggregates result Server / Principal Agents

3
Raphael Eidenbenz, GameNets ‘093 Introduction: Grid Computing –What are an agent‘s incentives? Payment, fame, altruism –Why not cheat and return a random result? Will principal find out? Not really –Individual computation is a hidden action –Principal can only check whether entire project failed Server / Principal Agents

4
Raphael Eidenbenz, GameNets ‘094 Introduction: Grid Computing –Project failed Who did a bad job? Whom to pay? –Maybe project still succeeds if only one agent exerts low effort If more than 2/3 of the agents exert high effort... Whom to pay? Server / Principal Agents

5
Raphael Eidenbenz, GameNets ‘095 Binary Combinatorial Agency [Babaioff, Feldman, Nisan 2006] 1 principal, n selfish risk-neutral agents Hidden actions ={ high effort, low effort } –High effort subtask succeeds with probability δ –Low effort subtask succeeds with probability γ Combinatorial project success function –AND: success if all subtasks succeed –OR: success if at least one subtask succeeds –MAJORITY: success if more than half of the agents succeed Principal contracts with agents –Individual payment p i depending on entire project‘s outcome –Assume Nash equilibrium in the created game

6
Raphael Eidenbenz, GameNets ‘096 Results [Babaioff, Feldman, Nisan 2006] AND technology –Principal either contracts with all agents or with none Depending on her valuation v –One transition point where optimal choice changes OR technology –Principal contracts with k agents, 0 · k · n –With increasing valuation v, there are n transition points where the optimal number k increases by 1

7
Raphael Eidenbenz, GameNets ‘097 Combinatorial Agency with Audits Grid computing: server can recompute a subtask –Actions are observable at a certain cost κ. –Principal conducts k random audits among the l contracted agents Agent i is audited with probability –Sophisticated contracts If audited and convicted of low effort ! p i =0 even if project successful Server / Principal Agents

8
Raphael Eidenbenz, GameNets ‘098 Some Observations The possibility of auditing can never be detrimental Nash Equilibrium if principal contracts l and audits k agents –payment p i –principal utility u –agent utility u i

9
Raphael Eidenbenz, GameNets ‘099 AND-Technology Project succeeds if all agents succeed δ: agent success probability with high effort γ: agent success probability with low effort There is one transition point v * –for v · v *, contract no agent –for v ¸ v *, contract with all agents and conduct k * audits Transition earlier with the leverage of audits Theorem

10
Raphael Eidenbenz, GameNets ‘0910 AND-Technology (2 Agents): Principal Utility

11
Raphael Eidenbenz, GameNets ‘0911 AND-Technology: Benefit from Audits in %

12
Raphael Eidenbenz, GameNets ‘0912 OR-Technology Project succeeds if at least one agent succeeds δ: agent success probability with high effort γ: agent success probability with low effort There are n transition point v 1 *,v 2 *,...,v n * –for v · v 1 *, contract no agent –for v l-1 * · v · v l *, contract with l agents, conduct k * (l) audits –for v ¸ v n *, contract with all agents and conduct k * (n) audits Conjecture Lemma

13
Raphael Eidenbenz, GameNets ‘0913 OR-Technology (2 Players): Benefit from Audits in %

14
Raphael Eidenbenz, GameNets ‘0914 Conclusion If hidden actions can be revealed at a certain cost, the coordinator may improve cooperation and efficiency in a distributed system AND technology –General solution to optimally choose p i, l and k –One transition point with increasing valuation OR technology –Formula for number of audits to conduct if number of contracts given Principal can find optimal solution in O(n) –Probably n transition points Transition points occur earlier with the leverage of audits

15
Raphael Eidenbenz, GameNets ‘0915 Outlook Test results in the wild –Accuracy of the model? –Does psychological aversion against control come into play? Non-anonymous technologies –Which set of agents to audit? Solve problem independent of technology –Are there general algorithms to solve the principal‘s optimization problem for arbitrary technologies? –What is the complexity? Total rationality unrealistic Thank you!

16
Raphael Eidenbenz, GameNets ‘0916 Bibliography [Babaioff, Feldman, Nisan 2006]: Combinatorial Agency. EC 2006. [Babaioff, Feldman, Nisan 2006]: Mixed Strategies in Combinatorial Agency. WINE 2006. [Monderer, Tennenholtz]: k-Implementation. EC 2003. [Enzle, Anderson]: Surveillant Intentions and Intrinsic Motivation. J. Personality and Social Psychology 64, 1993. [Fehr, Klein, Schmidt]: Fairness and Contract Design. Econometrica 75, 2007.

17
Raphael Eidenbenz, GameNets ‘0917 Outline Introduction: Grid Computing Combinatorial Agency –Binary Model –Results by Babaioff, Feldman, Nisan Combinatorial Agency with Audits –First Facts –AND technology –OR technology Conclusion Outlook

18
Raphael Eidenbenz, GameNets ‘0918 Anonymous Technologies Success function t depends only on number of agents exerting high effort –t m : success probability if m agents exert high effort Optimal payments Principal utility Optimal #audits

19
Raphael Eidenbenz, GameNets ‘0919 AND-Technology Project succeeds if all agents succeed Success function t m =δ m ¢ γ n-m There is one transition point v * –for v · v *, contract no agent –for v ¸ v *, contract with all agents and conduct k * audits Theorem

20
Raphael Eidenbenz, GameNets ‘0920 AND-Technology: Principal Utility

21
Raphael Eidenbenz, GameNets ‘0921 MAJORITY Technology Optimal payment where Principal utility

Similar presentations

OK

PODC 20081 Distributed Computation of the Mode Fabian Kuhn Thomas Locher ETH Zurich, Switzerland Stefan Schmid TU Munich, Germany TexPoint fonts used in.

PODC 20081 Distributed Computation of the Mode Fabian Kuhn Thomas Locher ETH Zurich, Switzerland Stefan Schmid TU Munich, Germany TexPoint fonts used in.

© 2017 SlidePlayer.com Inc.

All rights reserved.

Ads by Google